AARHUS

UNIVERSITY

M. Kargo1,3, J. F. Ettema1,2, L. Hjortø1, J. Pedersen3 , and S. Østergaard1 1 Aarhus University, Denmark, 2 SimHerd Inc., Denmark, 3 SEGES, Denmark

Derivation of economic values for breeding goal traits in four different production systems

(The optimal cow)

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UNIVERSITY

• The ideal way:

– Derive marginal economic value, keeping

the remaining traits constant

• Wolfova and Wolf (2013, Animal)

– On the issue of double counting

• Do not include genetic correlations in the derivation

• Include structural changes in the derivation

Breeding goal - theory

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UNIVERSITY

Structural relationships

an example Improved health

Longer lasting cows

The consequence is lower weight on

longevity, because the weights is put were it

belongs to.

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• Experience from the NTM work:

– Interactions between yield, functional traits and longevity are difficult to handle.

Breeding goal - practice

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Method

run

Incidence of

metritis

Net return

per

cow-year

run run

Incidence

of metritis

Simulation years 11-20

• Mechanistic, dynamic and stochastic simulation in SimHerd (Østergård et al., 2014, Østergård et al., 2016 (JDS))

– Phenotypic correlations included

– Structural interactions included

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Method

run

Incidence of

metritis

yield

€ metritis

metritis €

• direct effect of X on Y = c

• indirect effect of X on Y = a * b

• direct effect of X on Y with the effect of the mediator removed = c’

Fairchild and MacKinnon, 2009

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UNIVERSITY

Investigated production systems • Conventional

– Average Danish, conventional dairy herd in term of production, reproduction and health

• Organic – Organic milk level, slightly better health, higher prices for

milk and feed

• Environment – High management level and use of beef semen to reduce

young stock herd

• Hi-Tec – High management level due to low disease treatment

threshold and automatic heat detection

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Results – Selected traits for HF Relative economic values across environments within

traits

Trait Conv. Organic Hitec Env.

Yield 100 121 93 98

Feed efficiency 100 123 103 101

Cow mortality 100 102 112 114

Milk fewer 100 338 202 99

Mastitis (infectious) 100 205 109 108

Digetal Dermititis 100 101 81 100

Conception rate, cows 100 48 82 133

Conception rate, heifers 100 110 106 65

Longevity 100 108 121 135

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Explanations - yield

• Organic: High EV’s because of higher prices for organic milk and higher costs for organic feed

9

Trait Conv. Organic Hitec Env.

Yield 100 121 93 98

Feed efficiency 100 123 103 101

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Explanations - Health

• Organic: High EVs due to restrictions on use of antibiotics

• Hitec: High EV for milk fewer because of more older cows

Trait Conv. Organic Hitec Env.

Milk fever 100 338 202 99

Mastitis (infectious) 100 205 109 108

Digital Dermititis 100 101 81 100

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Conclusion

• The derived EV’s are VERY dependent on production assumptions

• The estimated correlations between the four different breeding goals are quite high

• Including farmer preferences may alter this • Including G*E interactions may alter this • YES this can in combination with the present

excel sheet be used for an update of NTM • Division of breeds in lines require more

investigations (e.g. SOBcows)

SOBcows Specialized Organic Breeding goals and

Breeding schemes within dairy production

M. Kargo, L. Hjortø, M. Slagboom, J. R. Thomasen, A. Buitenhuis, L. Hein, J. Pedersen, A Munk and others

26th of April 2016

Overall goal

• To enhance the size and the profitability of the organic dairy production

– By adapting the breeding material to organic production circumstances

– By indicating sustainable methods for sustainable niche production based on animals with specific genetic characteristics

Definition of organic breeding goals

• Margots presentation

Breeding Schemes

• Can one or more of the breeds be divided in more lines? • Are the breeding goals sufficiently different?

• Are the populations big enough?

• Criteria: • Genetic gain

• Rate of inbreeding

...|

Division of dairy cattle breeding

goal?

• Before the genomic era – Many progeny tested bulls needed for substantial Δ G

– Big populations needed

– Break-even correlation appr. 0.85 (Depending on pop. size)

• Today – Good reference populations needed

• Much smaller than the number of test daughters needed before

– Genomic tests cost money

– Break-even correlation >> 0.85

16

Registered production cows

The drive of genetic gain – before GS

Number of progeny tested YB

PB

Sele

ctio

n

inte

nsity

A

ccu

racy

Anders Christian Sørensen and Jørn Thomasen

Production cows

Reference population

The drive of genetic gain – using GS

Genotyped bullcalves

GVP

Sele

ctio

n in

ten

sity

Genomic young bull scheme

Accuracy Anders Christian Sørensen and Jørn Thomasen

Illustration of line division

United population

Time

Conventional

Organic

Organic

?

Questions to be answered within each breed

• Effect of breeding goal

• Effect of G by E interactions

• Reference population • Conventional

• Conventional and Organic

• Recruitment strategy • Conventional

• Conventional and Organic

WP1 - Status

• Farmer survey finished

• In the process of defining breeding goals based on organic principles

• Gains for different BG will be simulated

• Based on that, BG’ for further simulation will be selected

• Collaboration with SLU, Sweden

WP2

• Breeding values for Health promoting fatty acids

– Traditional

– Genomic

• Business plan for organic niche production based on health promoting fatty acids

• C14:0 • C16:0 • C18:0 • C18:1 • SFA • MUFA • PUFA • SCFA (C4,C6,C8&C10) • MCFA (C12,C14&C16) • LCFA (C18 or longer) • Trans FA

SFA

MCFA SCFA

C14:0 C16:0

MUFA PUFA

C18:1

LCFA

C18:0

Trans FA

App.70%

App. 2.7%

n.o.i.

Good

Bad?

Application note 64 FOSS

0

20000

40000

60000

80000

100000

120000

140000

160000

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7

MUFA

Lisa Hein, Lars Peter Sørensen and Jørn Pedersen

0

10

20

30

40

50

60

70

80

SFA MUFA PUFA SCFA LCFA MCFA Trans FA C18:1 C16:0 C14:0 C18:0

Organic

RDM

Holstein

Jersey

Krydsning

Means of fatty acids

Lisa Hein, Lars Peter Sørensen and Jørn Pedersen

WP2

• Review on the value of fatty acids carried out

• Collection of fatty acids content in milk since May 2015

• Strategy for genomic test in place May 2016

• Report on fatty acids – May 2016

• Breeding values for fatty acids August 2016

• To be included in the BG

• Starting work on business plan for milk with health promoting qualities in the autumn

WP3 • 22 heifers of original Danish breeds

transferred to 5 Naturmælk herd

– Still 10 to be moved